mlsanityData to 14 Oct 2026

Radar Tracker: Moving Instance Tracking in Sparse and Noisy Radar Point Clouds

arXiv 2507.03441 ↗First posted 4 Jul 2025cs.CVcited by 2

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Abstract (arXiv)

Robots and autonomous vehicles should be aware of what happens in their surroundings. The segmentation and tracking of moving objects are essential for reliable path planning, including collision avoidance. We investigate this estimation task for vehicles using radar sensing. We address moving instance tracking in sparse radar point clouds to enhance scene interpretation. We propose a learning-based radar tracker incorporating temporal offset predictions to enable direct center-based association and enhance segmentation performance by including additional motion cues. We implement attention-based tracking for sparse radar scans to include appearance features and enhance performance. The final association combines geometric and appearance features to overcome the limitations of center-based tracking to associate instances reliably. Our approach shows an improved performance on the moving instance tracking benchmark of the RadarScenes dataset compared to the current state of the art.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 42 of its 58 references that are in the map. First posted on arXiv 4 Jul 2025.

Builds on

42 of 58 references.

References · 42

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30 more
  1. 2022Mask3D: Mask Transformer for 3D Semantic Instance Segmentationcited by 303
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  27. 2022Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Datacited by 15
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  29. 2023Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Cloudscited by 9
  30. 2023Lidar Panoptic Segmentation and Tracking without Bells and Whistlescited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025SemRaFiner: Panoptic Segmentation in Sparse and Noisy Radar Point Cloudscited by 2
  2. 2026ModTrack: Sensor-Agnostic Multi-View Tracking via Identity-Informed PHD Filtering with Covariance Propagationcited by 0